Look at the available datasets
Loading the Data
#incoming data comes in as a list
datasets <- tidytuesdayR::tt_load("2020-08-11")
Downloading file 1 of 2: `avatar.csv`
Downloading file 2 of 2: `scene_description.csv`#show the names of the individual datasets
names(datasets)
[1] "avatar" "scene_description"avatar <- datasets$avatar
avatar[1:5,]
# A tibble: 5 × 11
id book book_num chapter chapter_num character full_text
<dbl> <chr> <dbl> <chr> <dbl> <chr> <chr>
1 1 Water 1 The Boy … 1 Katara "Water. Earth…
2 2 Water 1 The Boy … 1 Scene Des… "As the title…
3 3 Water 1 The Boy … 1 Sokka "It's not get…
4 4 Water 1 The Boy … 1 Scene Des… "The shot pan…
5 5 Water 1 The Boy … 1 Katara "[Happily sur…
# … with 4 more variables: character_words <chr>, writer <chr>,
# director <chr>, imdb_rating <dbl>scenes <- datasets$scene_description
scenes[1:5,]
# A tibble: 5 × 2
id scene_description
<dbl> <chr>
1 3 [Close-up of the boy as he grins confidently over his shoulde…
2 5 [Happily surprised.]
3 6 [Close-up of Sokka; whispering.]
4 6 [A look of bliss adorns his face. He licks his lips and wiggl…
5 8 [Struggling with the water that passes right in front of her.]My Research Question
Does the sentiment of each character change over the multiple seasons? That is, does a character become more positive or more negative as their character develops?
I will attempt to summarize the sentiment of each character across each episode.
Using tidytext to unnest_tokens() - that is, split each line into 1 word per row.
library(tidytext)
library(tidyverse)
avatar_words <- avatar %>%
select(id, book, book_num, chapter, chapter_num, character, character_words) %>%
filter(character != "Scene Description") %>%
unnest_tokens(word, character_words)
avatar_words[1:10,]
# A tibble: 10 × 7
id book book_num chapter chapter_num character word
<dbl> <chr> <dbl> <chr> <dbl> <chr> <chr>
1 1 Water 1 The Boy in the… 1 Katara water
2 1 Water 1 The Boy in the… 1 Katara earth
3 1 Water 1 The Boy in the… 1 Katara fire
4 1 Water 1 The Boy in the… 1 Katara air
5 1 Water 1 The Boy in the… 1 Katara my
6 1 Water 1 The Boy in the… 1 Katara grandmo…
7 1 Water 1 The Boy in the… 1 Katara used
8 1 Water 1 The Boy in the… 1 Katara to
9 1 Water 1 The Boy in the… 1 Katara tell
10 1 Water 1 The Boy in the… 1 Katara me Characters by Episode/Chapter
Bit Players
episode_count %>%
filter(n == 1) %>%
arrange(character)
# A tibble: 272 × 2
character n
<chr> <int>
1 Aang and Zuko 1
2 Aang: 1
3 Actor Bumi 1
4 Actor Iroh 1
5 Actor Jet 1
6 Actor Ozai 1
7 Actor Sokka 1
8 Actor Toph 1
9 Actor Zuko 1
10 Actress Azula 1
# … with 262 more rowsCabbage merchant
The cabbage merchant appears in 4 episodes, and you can see his path to resignation as Aang and company keep busting up his cabbage kiosk.
avatar %>%
filter(character == "Cabbage merchant") %>%
select(chapter, character_words) %>%
gt::gt()
| chapter | character_words |
|---|---|
| The King of Omashu | No! My cabbages! |
| The King of Omashu | My cabbages! You're gonna pay for this! |
| The King of Omashu | Off with their heads! One for each head of cabbage! |
| The King of Omashu | My cabbages! |
| The Waterbending Scroll | My cabbages! This place is worse than Omashu! |
| The Serpent's Pass | Ahhh! My cabbages! |
| The Tales of Ba Sing Se | My cabba- Oh, forget it. |
Who Spoke the Most?
Surprisingly, Sokka has the most lines.
line_count <- avatar_words %>%
count(character) %>%
arrange(desc(n))
line_count[1:20,] %>%
gt::gt()
| character | n |
|---|---|
| Sokka | 18293 |
| Aang | 17821 |
| Katara | 14961 |
| Zuko | 8972 |
| Toph | 5434 |
| Iroh | 5252 |
| Azula | 3299 |
| Zhao | 1607 |
| Jet | 1604 |
| Suki | 1221 |
| Hakoda | 1065 |
| Pathik | 1030 |
| Roku | 1015 |
| Ozai | 1002 |
| Hama | 955 |
| Mai | 844 |
| Bumi | 818 |
| Long Feng | 757 |
| Warden | 722 |
| Ty Lee | 705 |
Understanding Each Character’s Journey
Using tidytext, I do a sentiment analysis of each episode (here called a chapter) to determine the overal sentiment for a character.
bing <- get_sentiments("bing")
characters <- c("Aang", "Katara", "Zuko", "Toph", "Iroh", "Sokka", "Azula", "Mai", "Ty Lee")
sentiment_summary <- avatar_words %>%
inner_join(bing) %>%
count(book_num, chapter_num, chapter, character, sentiment) %>%
filter(character %in% characters) %>%
arrange(book_num, chapter_num) %>%
pivot_wider(names_from = sentiment, values_from = n) %>%
mutate(positive = tidyr::replace_na(positive, 0),
negative = tidyr::replace_na(negative, 0)) %>%
mutate(sentiment = positive - negative)
index_chapters <- avatar_words %>%
select(book_num, chapter_num) %>%
distinct() %>%
mutate(index = row_number())
sentiment_index <- sentiment_summary %>%
inner_join(y= index_chapters, by=c("book_num", "chapter_num"))
out_plot <- ggplot(sentiment_index) +
aes(x=index, y=sentiment, fill=character, episode=chapter, book = book_num, episode_number=chapter_num) +
geom_col(show_legend = FALSE) +
facet_wrap(~character, ncol=2) +
labs(title= "Each Character's Sentiment Journey", x="Episode Number",
subtitle = "mouse over each graph for more information") +
geom_vline(xintercept = 21, lty=2) +
geom_vline(xintercept = 41, lty=2)
plotly::ggplotly(out_plot)
Sentiment Heatmap
s_index <- sentiment_index %>%
tidyr::complete(chapter_num, character)
ggplot(s_index) +
aes(x=index, y=character, fill=sentiment) +
geom_tile() +
scale_fill_viridis_b(na.value="black")
Zuko has the most interesting journey
Zuko has many ups and downs, which may reflect his overall lack of confidence and his tendency for self-loathing.
zuko <- sentiment_index %>%
filter(character=="Zuko")
out_plot <- ggplot(zuko) +
aes(x=index, y=sentiment, fill=character, episode=chapter, book = book_num, group=character, episode_number=chapter_num) +
geom_col(show_legend = FALSE) +
facet_wrap(~character, ncol=2) +
annotate(geom="text", x=27, y= -8 , label = "Zuko Alone\nA Turning Point") +
annotate(geom="text", x=53, y = 11, label = "Where Zuko\ntrains Aang") +
labs(title= "Zuko has lots of ups and downs", x="Episode Number",
subtitle = "mouse over for more episode information") +
ylim(c(-13, 13)) +
geom_vline(xintercept = 21, lty=2) +
geom_vline(xintercept = 41, lty=2)
plotly::ggplotly(out_plot)
Aang and Zuko’s Journeys
Plotting the sentiment journey of Zuko and Aang together shows that they often mirror each other, except in the last parts of Book 3.
zuko_aang <- sentiment_index %>%
filter(character %in% c("Zuko", "Aang"))
out_plot <- ggplot(zuko_aang) +
aes(x=index, y=sentiment, fill=character, episode=chapter, book = book_num, episode_number=chapter_num) +
geom_col(show_legend = FALSE, alpha=0.7) +
labs(title= "Aang and Zuko's Journeys Often Mirror Each Other",
x="Episode Number",
subtitle = "mouse over for more episode information") +
ylim(c(-13, 13)) +
geom_vline(xintercept = 21, lty=2) +
geom_vline(xintercept = 41, lty=2)
plotly::ggplotly(out_plot)
Iroh is so chill and positive
iroh <- sentiment_index %>%
filter(character=="Iroh")
out_plot <- ggplot(iroh) +
aes(x=index, y=sentiment, fill=character, episode=chapter, book = book_num, episode_number=chapter_num) +
geom_col(show_legend = FALSE) +
labs(title= "Iroh is just so chill and positive", x="Episode Number",
subtitle = "mouse over for more episode information") +
ylim(c(-13, 13)) +
geom_vline(xintercept = 21, lty=2) +
geom_vline(xintercept = 41, lty=2)
plotly::ggplotly(out_plot)